TESTING DIFFERENT CLASSIFICATION

APPROACHES BASED ON FACE RECOGNITION

APPLICATIONAHMED HELMI ABULILA

MOTIVATION

• Get the advantage of ECE539 gained skills .

• Comparing different classifiers accuracy, trying to answer the question of “Which Classifier should I use?”.

WHY FACE RECOGNITION

• One of the most famous pattern recognition application.

• Market demand application.

• Able to measure the accuracy of a classifier.

DATASET

• My dataset is frontal face dataset, collected by Markus Weber at Caltech.

• It consists of 450 face image of 27, or so, unique people under different lighting, expressions, and backgrounds.

• The dataset can be downloaded from Caltech 101 website.

DATASET PREPROCESSING

• One of the pattern recognition challenges.

• Don’t worry, it is one of ECE539 gained skills.

• The # of features affects on the classifier learning process. If it is huge, it will take a long time during the learning phase to give an acceptable result. On the other hand, if it is too small, it will not be able to learn.

• The dataset, which is used in this project, has a lot of preprocessing data to minimize the # of features, which is 896 x 592, without affecting the info. Needed by the classifier, like disregarding the background.

DATA PREPROCESSING STEP ONE (HARPIA TOOL)

DATA PREPROCESSING STEP ONE (HARPIA TOOL)

DATA PREPROCESSING STEP ONE (HARPIA TOOL)

DATA PREPROCESSING STEP ONE (HARPIA TOOL)

C

code

DATA PREPROCESSING STEP TWO

• The # of features (175x175) is still high, by using Matlab™, a simple code can reduce the size of these images and convert it to a vector, which is the final step in the data preprocessing phase.

• The data is going to be introduced in two different reduction rate to measure the effect of the reduction on the classifier accuracy. The first one will be reduced by 2, and the other one will be reduced by 4.

CURRENT STATE

• Preprossing Phase is done.

• Working on choosing the best classifier.

CLASSIFIER LEARNING PHASE

• The two preprocessed data are going to be used by three different classifiers.

• The result is going to be used to compare the performance of the three classifiers and the effect of the reduction in the final result.

THANK YOU